Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Reduced Fluid Dynamics
Authors: Zherong Pan, Xifeng Gao, Kui Wu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through evaluations on a row of simulation benchmarks, we show that our method reduces the discrepancy by 50-90 percent over conventional reduced models and we outperform PINNs by exactly preserving the time reversibility. |
| Researcher Affiliation | Industry | Lightspeed Studios EMAIL |
| Pseudocode | No | The paper mentions outlining an algorithm in Appendix 1 but does not present structured pseudocode or an algorithm block in the main text. |
| Open Source Code | No | The paper does not provide any statement or link indicating the public release of source code for the described methodology. |
| Open Datasets | No | The paper uses simulation benchmarks like 'Taylor vortices' and 'smoke plume rise' and states, 'Our training dataset for the POD baseline contains N = 8 trajectories using the full-order dynamics (Equation 1)', but does not provide specific access information (link, DOI, repository, or formal citation with authors/year) for these simulation data. |
| Dataset Splits | No | The paper mentions training and testing data, but does not explicitly describe a validation dataset split or a cross-validation setup. |
| Hardware Specification | Yes | We implement our method using Pytorch with a fluid simulator implemented via native C++ with CPU parallelization, and perform all the computations on an AMD Threadripper 3970X CPU having 32 cores. |
| Software Dependencies | No | The paper states 'We implement our method using Pytorch with a fluid simulator implemented via native C++', but does not provide specific version numbers for Pytorch, C++, or any other software dependencies. |
| Experiment Setup | Yes | We always use a batch size of 1. The performance of our method is summarized in Table 2. We consider two variants of our method: coupled case, where Ckij is treated as a function C(Uk,Ui,Uj) as discussed in Section , and decoupled case, where Ckij is treated as an antisymmetric independent decision variable. ... We experiment with four parameters ϵ = 0.05, 0.01, 0.001, and 0.0001 and the number of bases is p = 8, 11, 16, and 25, correspondingly. ... we set T = 500, δt = 0.01. |